3,959 research outputs found
A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection
The broadening dependency and reliance that modern societies have on essential services
provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical
Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just
at the economic level but also in terms of physical damage and even loss of human life. Complementing
traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring
Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are
in place and compliant with standards and internal policies. Forensics assist the investigation of past security
incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can
be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the
latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing
in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of
tackling the requirements imposed by massively distributed and complex Industrial Automation and Control
Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and
redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced
a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the
collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic
template for a converged platform. These results are intended to guide future research on forensics and
compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio
Documenting Knowledge Graph Embedding and Link Prediction using Knowledge Graphs
In recent years, sub-symbolic learning, i.e., Knowledge Graph Embedding (KGE) incorporated with Knowledge Graphs (KGs) has gained significant attention in various downstream tasks (e.g., Link Prediction (LP)). These techniques learn a latent vector representation of KG's semantical structure to infer missing links. Nonetheless, the KGE models remain a black box, and the decision-making process behind them is not clear. Thus, the trustability and reliability of the model's outcomes have been challenged. While many state-of-the-art approaches provide data-driven frameworks to address these issues, they do not always provide a complete understanding, and the interpretations are not machine-readable. That is why, in this work, we extend a hybrid interpretable framework, InterpretME, in the field of the KGE models, especially for translation distance models, which include TransE, TransH, TransR, and TransD. The experimental evaluation on various benchmark KGs supports the validity of this approach, which we term Trace KGE. Trace KGE, in particular, contributes to increased interpretability and understanding of the perplexing KGE model's behavior
On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse
This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new
experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse
Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review
Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, which allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects
Explainable Representations for Relation Prediction in Knowledge Graphs
Knowledge graphs represent real-world entities and their relations in a
semantically-rich structure supported by ontologies. Exploring this data with
machine learning methods often relies on knowledge graph embeddings, which
produce latent representations of entities that preserve structural and local
graph neighbourhood properties, but sacrifice explainability. However, in tasks
such as link or relation prediction, understanding which specific features
better explain a relation is crucial to support complex or critical
applications.
We propose SEEK, a novel approach for explainable representations to support
relation prediction in knowledge graphs. It is based on identifying relevant
shared semantic aspects (i.e., subgraphs) between entities and learning
representations for each subgraph, producing a multi-faceted and explainable
representation.
We evaluate SEEK on two real-world highly complex relation prediction tasks:
protein-protein interaction prediction and gene-disease association prediction.
Our extensive analysis using established benchmarks demonstrates that SEEK
achieves significantly better performance than standard learning representation
methods while identifying both sufficient and necessary explanations based on
shared semantic aspects.Comment: 16 pages, 3 figure
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Notation3 as an Existential Rule Language
Notation3 Logic (\nthree) is an extension of RDF that allows the user to
write rules introducing new blank nodes to RDF graphs. Many applications (e.g.,
ontology mapping) rely on this feature as blank nodes -- used directly or in
auxiliary constructs -- are omnipresent on the Web. However, the number of fast
\nthree reasoners covering this very important feature of the logic is rather
limited. On the other hand, there are engines like VLog or Nemo which do not
directly support Semantic Web rule formats but which are developed and
optimized for very similar constructs: existential rules. In this paper, we
investigate the relation between \nthree rules with blank nodes in their heads
and existential rules. We identify a subset of \nthree which can be mapped
directly to existential rules and define such a mapping preserving the
equivalence of \nthree formulae. In order to also illustrate that in some cases
\nthree reasoning could benefit from our translation, we then employ this
mapping in an implementation to compare the performance of the \nthree
reasoners EYE and cwm to VLog and Nemo on \nthree rules and their mapped
counterparts. Our tests show that the existential rule reasoners perform
particularly well for use cases containing many facts while especially the EYE
reasoner is very fast when dealing with a high number of dependent rules. We
thus provide a tool enabling the Semantic Web community to directly use
existing and future existential rule reasoners and benefit from the findings of
this active community
Trusted Provenance with Blockchain - A Blockchain-based Provenance Tracking System for Virtual Aircraft Component Manufacturing
The importance of provenance in the digital age has led to significant interest in utilizing blockchain technology for tamper-proof storage of provenance data. This thesis proposes a blockchain-based provenance tracking system for the certification of aircraft components. The aim is to design and implement a system that can ensure the trustworthy, tamper-resistant storage of provenance documents originating from an aircraft manufacturing process. To achieve this, the thesis presents a systematic literature review, which provides a comprehensive overview of existing works in the field of provenance and blockchain technology. After obtaining strategies to utilize blockchain for the storage of provenance data on the blockchain, a system was designed to meet the requirements of stakeholders in the aviation industry. The thesis utilized a systematic approach to gather requirements by conducting interviews with stakeholders. The system was implemented using a combination of smart contracts and a graphical user interface to provide tamper-resistant, traceable storage of relevant data on a transparent blockchain. An evaluation based on the requirements identified during the requirement engineering process found that the proposed system meets all identified requirements. Overall, this thesis offers insight into a potential application of blockchain technology in the aviation industry and provides a valuable resource for researchers and industry professionals seeking to leverage blockchain technology for provenance tracking and certification purpose
Defining Safe Training Datasets for Machine Learning Models Using Ontologies
Machine Learning (ML) models have been gaining popularity in recent years in a wide variety of domains, including safety-critical domains. While ML models have shown high accuracy in their predictions, they are still considered black boxes, meaning that developers and users do not know how the models make their decisions. While this is simply a nuisance in some domains, in safetycritical domains, this makes ML models difficult to trust. To fully utilize ML models in safetycritical domains, there needs to be a method to improve trust in their safety and accuracy without human experts checking each decision. This research proposes a method to increase trust in ML models used in safety-critical domains by ensuring the safety and completeness of the model’s training dataset. Since most of the complexity of the model is built through training, ensuring the safety of the training dataset could help to increase the trust in the safety of the model. The method proposed in this research uses a domain ontology and an image quality characteristic ontology to validate the domain completeness and image quality robustness of a training dataset. This research also presents an experiment as a proof of concept for this method where ontologies are built for the emergency road vehicle domain
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